English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Drift in machine learning refers to the phenomenon where the statisticalproperties of data or context, in which the model operates, change over timeleading to a decrease in its performance. Therefore, maintaining a constantmonitoring process for machine learning model performance is crucial in orderto proactively prevent any potential performance regression. However,supervised drift detection methods require human annotation and consequentlylead to a longer time to detect and mitigate the drift. In our proposedunsupervised drift detection method, we follow a two step process. Our firststep involves encoding a sample of production data as the target distribution,and the model training data as the reference distribution. In the second step,we employ a kernel-based statistical test that utilizes the maximum meandiscrepancy (MMD) distance metric to compare the reference and targetdistributions and estimate any potential drift. Our method also identifies thesubset of production data that is the root cause of the drift. The modelsretrained using these identified high drift samples show improved performanceon online customer experience quality metrics.

Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models